Forecasting Green Chilli Prices: Using Data Analytics to Gain Market Understanding
Midathana Anil Kumar *
Department of Agricultural Statistics, Uttar Banga Krishi Viswavidyalaya, Cooch Behar-736165, West Bengal, India.
Arunava Ghosh
Department of Agricultural Statistics, Uttar Banga Krishi Viswavidyalaya, Cooch Behar-736165, West Bengal, India.
Vinay H T
Department of Agricultural Statistics, Uttar Banga Krishi Viswavidyalaya, Cooch Behar-736165, West Bengal, India.
Parthendu Poddar
Department of Agronomy, Uttar Banga Krishi Viswavidyalaya, Cooch Behar-736165, West Bengal, India.
Md. Wasim Reza
Regional Research Sub-station (Terai Zone), Uttar Banga Krishi Viswavidyalaya, Cooch Behar-736165, West Bengal, India.
*Author to whom correspondence should be addressed.
Abstract
Green chilli is a commercially significant vegetable crop grown year-round due to its high demand for both nutritional and health benefits. India stands as the largest producer and consumer of chilli globally, with West Bengal leading in area under cultivation and ranking sixth in production. This study aims to compare and identify the most accurate model for forecasting green chilli prices in the Haldibari market of Cooch Behar district, West Bengal. Price data from January 2015 to May 2024, sourced from AGMARKNET, was used for model development, with 85% of the data allocated for training and 15% for validation. The models tested include the traditional Seasonal Auto-Regressive Integrated Moving Average (SARIMA) and machine learning models like Artificial Neural Networks (ANN) and Support Vector Regression (SVR). Among these, the ANN model is found to be the most accurate, with a low Mean Absolute Percentage Error (MAPE) of 0.14. The model is used to forecast prices for the next 12 months, up to May 2025. The study’s findings can aid farmers and policymakers in creating effective crop planning strategies, helping to boost local farm income.
Keywords: Price, forecasting, SARIMA, ANN, SVR